I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end-effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.201707035

Soft Robotic Grippers.

As the field of robotics is expanding from the fixed environment of a production line to complex human environments, robots are required to perform increasingly human-like manipulation tasks, moving the state-of-the-art in robotics from grasping to advanced in-hand manipulation tasks such as regrasping, rotation and translation. To achieve advanced in-hand manipulation tasks, robotic hands are required to be equipped with distributed tactile sensing that can continuously provide information about the magnitude and direction of forces at all contact points between them and the objects they are interacting with. This paper reviews the state-of-the-art in force and tactile sensing technologies that can be suitable within the specific context of dexterous in-hand manipulation. In previous reviews of tactile sensing for robotic manipulation, the specific functional and technical requirements of dexterous in-hand manipulation, as compared to grasping, are in general not taken into account. This paper provides a review of models describing human hand activity and movements, and a set of functional and technical specifications for in-hand manipulation is defined. The paper proceeds to review the current state-of-the-art tactile sensor solutions that fulfil or can fulfil these criteria. An analytical comparison of the reviewed solutions is presented, and the advantages and disadvantages of different sensing technologies are compared.

Tactile sensing for dexterous in-hand manipulation in robotics-A review

This paper provides a review of recent developments in the rapidly changing and advancing field of smart fabric sensor and electronic textile technologies. It summarizes the basic principles and approaches employed when building fabric sensors as well as the most commonly used materials and techniques used in electronic textiles. This paper shows that sensing functionality can be created by intrinsic and extrinsic modifications to textile substrates depending on the level of integration into the fabric platform. The current work demonstrates that fabric sensors can be tailored to measure force, pressure, chemicals, humidity and temperature variations. Materials, connectors, fabric circuits, interconnects, encapsulation and fabrication methods associated with fabric technologies prove to be customizable and versatile but less robust than their conventional electronics counterparts. The findings of this survey suggest that a complete smart fabric system is possible through the integration of the different types of textile based functional elements. This work intends to be a starting point for standardization of smart fabric sensing techniques and e-textile fabrication methods.

https://iopscience.iop.org/article/10.1088/0964-1726/23/5/053001/pdf

Smart fabric sensors and e-textile technologies: a review

Tactile sensing in dexterous robot hands - Review

We describe the development of a tactile hemispherical soft fingertip (FT) of a size similar to that of a human thumb. The sensory core consists of a microscaled force/torque sensor that can output one component of force and two components of moment simultaneously, which was developed beforehand. This sensor is embedded in a polyurethane rubber hemispherical dome to form a complete soft, compliant, and perceptible robotic FT. This system is designed for easy fabrication, high reliability in outputting signals, and stable operation. Static and dynamic mathematical analyses were utilized to investigate the responses of the sensor during the typical sliding motion of an FT. This was followed by experiments to show its potential in tactile and texture recognition. Especially, incipient-slip detection, which is critical in grasping manipulations, can be assessed properly and in a timely way. The development of this tactile FT is considered significant in the field of dexterous manipulation.

Development and Analysis of a Sliding Tactile Soft Fingertip Embedded With a Microforce/Moment Sensor

In this paper, we present a concept of using tactile sensors as sufficient tools in localizing, recognizing an object in robotic in-hand manipulation tasks. Our approach operates on a moderately intensive array data that are obtained from a tactile sensor when a robotic gripper grasps an object that is small relatively to fingers. In stead of using tactile data as an array of discrete numbers, we treat it as a grayscale image. By working with successive images from tactile sensor exploiting image processing tools, we are able to extract rich information about the contact condition between an object and the gripper. Experimental results show that from the processed data, we can realize the grasped object's position/orientation, contact shape, especially the stick-slip condition on the contact surface that is derived for the first time for this sensor. Based on localized displacement phenomenon of a sliding soft object, we proposed an approach to detect the slippage quantitatively, and conducted a wide range of experiment in term of characteristics of object's movement. We also conducted a model for an object-grasping gripper with tactile feedback in various postures of the object, and a corresponding experiment setup to validate computed results. Presented result is expected to be used widely in the community due to its simplicity and reliability.

What can be inferred from a tactile arrayed sensor in autonomous in-hand manipulation?

This paper presents a novel approach to the fabrication of a soft robotic hand with contact feedback for grasping delicate objects. Each finger has a multilayered structure, consisting of a main structure and sensing elements. The main structure includes a softer layer much thicker than a stiffer layer. The gripping energy of the fingers is generated from the elastic energy of the prestretched softer layers, and controlled by simple tendon strings pulled/released by a single actuation. Due to the prestretching and the difference in moduli among layers, the shape/posture of the fingers in stable state is similar to that of soft fingers actuated by pressurization. Then, we were able to design a soft-fingered hand for different applications by changing the morphological shape of layers. In addition, the hand includes a soft, flexible, and stretchable sensing element for the detection of contact and applied force, which can also be used in other designs of soft fingers. We assessed the ability of the proposed soft hand to grasp food products with feedback of contact location. The experimental results showed that the proposed hand could safely grasp light and delicate objects, such as fruits, and that it could possibly distinguish among objects based on feedback from sensors. The design proposed in this paper may give rise to other soft hand designs, along with the possibility of using morphological imbalanced deformation of multilayered structures in soft robotics research.

Design and Analysis of a Soft-Fingered Hand With Contact Feedback

The sense of touch allows individuals to physically interact with and better perceive their environment. Touch is even more crucial for robots, as robots equipped with thorough tactile sensation can more safely interact with their surroundings, including humans. This article describes a recently developed large-scale tactile sensing system for a robotic link, called TacLINK, which can be assembled to form a whole-body tactile sensing robot arm. The proposed system is an elongated structure comprising a rigid transparent bone covered by continuous artificial soft skin. The soft skin of TacLINK not only provides tactile force feedback but can change its form and stiffness by inflation at low pressure. Upon contact with the surrounding environment, TacLINK perceives tactile information through the three-dimensional (3-D) deformation of its skin, resulting from the tracking of an array of markers on its inner wall by a stereo camera located at both ends of the transparent bone. A finite element model (FEM) was formulated to describe the relationship between applied forces and the displacements of markers, allowing detailed tactile information, including contact geometry and distribution of applied forces, to be derived simultaneously, regardless of the number of contacts. TacLINK is scalable in size, durable in operation, and low in cost, as well as being a high-performance system, that can be widely exploited in the design of robotic arms, prosthetic arms, and humanoid robots, etc. This article presents the design, modeling, calibration, implementation, and evaluation of the system.

/pdf/large-scale-vision-based-tactile-sensing-for-robot-links-56ivmjqwla.pdf

Large-Scale Vision-Based Tactile Sensing for Robot Links: Design, Modeling, and Evaluation

Tactile sensing is an important ability for a humanoid robot which interacts in an unstructured environment. Such a system needs to sense and evaluate surface properties of objects that it interacts with. Among those properties, surface texture identification is a compulsory ability for certain kind of robot systems such as service robots, medical robots and exploratory robots. Therefore, the tactile system of above type of robots should have the ability to identify and discriminate textures with acceptable accuracy. A biomimetic fingertip that can be used in above kinds of robot tactile systems is introduced. The fingertip has the ability to detect force and vibration modalities. This paper reports the ability of the fingertip system to discriminate multiple materials (six fabrics and aluminium plate) by comparing the differences in their surface texture. The materials were classified using features: variance and power of the accelerometer signal. Moreover an Artificial Neural Network (ANN) classifier was evaluated by using the first 300 Fourier coefficients of the accelerometer signal as features. Above two methods used the raw signals of the accelerometers nearest to the contact area. Finally, an input signal was computed by calculating the covariance of two adjacent accelerometers. This new signal was used to calculate features for the ANN classifier. The results showed that the use of a convoluted signal improved the success rate of discriminating the seven textures.

Van Anh Ho

Papers

Development and Analysis of a Sliding Tactile Soft Fingertip Embedded With a Microforce/Moment Sensor

What can be inferred from a tactile arrayed sensor in autonomous in-hand manipulation?

Design and Analysis of a Soft-Fingered Hand With Contact Feedback

Large-Scale Vision-Based Tactile Sensing for Robot Links: Design, Modeling, and Evaluation

Investigation of a biomimetic fingertip's ability to discriminate fabrics based on surface textures